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AI in banking

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The next era of artificial intelligence (AI) and machine learning (ML) in banking will no doubt see improvements in customer service and operational efficiency. AI-powered customer servicing and  advisory will become more sophisticated and will be able to address more complicated scenarios.

On the operational side, AI will improve financial crime prevention by better detection of nefarious activities and improve lending decisions through more advanced modeling. Additionally, AI will further automate routine tasks such as data entry, contributing to more streamlined back-office processes.


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However, there are many challenges in scaling the adoption of AI in banking, including hurdles related to product, data, compliance, operations, and talent acquisition and training.

In this article, we will cover what the future of banking powered by AI and ML could look like, the challenges of getting there, and the critical capabilities needed to face those challenges.

AI has the potential to radically reshape how banks operate from front to back. Here are 4 key areas of the bank, and how they could be potentially affected by current and developing AI technologies.

AI in marketing and sales

  • Customer acquisition: Customer acquisition can be boosted through more effective targeting and predictive analytics, identifying potential clients more efficiently.
  • Customer onboarding: The onboarding process for consumers, businesses, and corporations could be fully automated in more difficult scenarios, making it faster and more user-friendly for new banking customers.
  • Customer loyalty: Customer loyalty could be enhanced with more personalized products and services, improving customer satisfaction and retention.

AI in operations and servicing

  • Advisory: Financial advice could be more intelligent and adaptive to changing conditions.
  • Processing: Exception handling in banking could be sped up, reducing wait times and improving operational efficiency.
  • Support: AI-powered assistants could handle more difficult customer inquiries and problems efficiently and effectively.

AI in risk and risk assessment

  • Modeling: While analyzing large data sets, risk modeling in banking could be much more robust and dynamic to predict and mitigate financial risks more accurately.
  • Debt collection: Debt collection strategies could be optimized by improved analysis and determining the most effective approach for repayment.
  • Financial crime prevention: AI could better detect financial crime by using pattern recognition to identify suspicious transactions and reduce false positives.

AI in finance and accounting

  • Reporting: Financial reporting in banking could be streamlined, automating data compilation and analysis for more accurate and timely reports. Additionally, reporting handled by AI agents would require fewer humans to come in contact with sensitive data.

Adapting to AI technology involves not only technical adjustments, but also shifts in customer expectations and organizational practices. As banks consider deeper integration inside the organization, it's important to recognize the challenges that may arise and be prepared to overcome them.


  • Customer adoption: Convincing customers to use AI-based services in banking can be challenging. Some customers might be reluctant to trust AI for their banking needs, making transparency and explainability important.


  • Quality: High-quality data is a prerequisite for AI in banking. Banks often face issues with data that is scattered, incomplete, or of low quality, posing challenges in developing effective AI models.
  • Legacy systems: Many banks operate with legacy systems that are not easily compatible with modern AI technologies. Integrating AI into these legacy systems can be costly, time-consuming, and complex.


  • Explainability: The complexity of AI algorithms using deep learning can make it hard to answer how an AI decision was made, which is a concern by regulators where transparency is required.
  • Privacy: Banking institutions handle sensitive customer data, and AI systems must ensure data privacy and security. This includes protecting data from being poisoned, which is a constant concern in the financial industry.
  • Responsible use:The use of AI in areas such as customer profiling and lending decisions can raise ethical concerns about fairness, discrimination, and privacy. Addressing these concerns is essential for successful adoption.

Infrastructure and Operations

  • Scalability: Expanding AI adoption throughout a banking organization, including business applications, and operations, is a significant challenge, especially when the pace of change continues to increase.
  • Cost: The initial investment and maintenance costs for AI in banking can be high, leading to hesitancy in adoption without clear evidence of beneficial ROI.


  • Talent acquisition: Banks face a shortage of AI professionals who are skilled in both data science and the banking business.
  • Employee adoption: Resistance to AI within banking organizations can arise from concerns about job displacement, job security, or a lack of understanding of AI benefits.

For banks to effectively use AI, certain critical capabilities are essential.

These range from technical aspects such as training and data management to organizational factors such as governance and talent acquisition. This section delves into these key capabilities, outlining what is necessary for banks to successfully implement and benefit from AI technologies. Understanding and developing these capabilities can significantly impact the effectiveness and efficiency of scaling AI across the bank.

Training and tuning

Effective AI use in banking requires robust foundation models and the capability to develop new ones. This necessitates a repository of foundational models that can be accessed and modified as needed. Additionally, the operating environment for training these models should be readily available and easily provisional. This helps the organization train and tune AI models efficiently, keeping pace with changing data and market conditions.

This agility in training and tuning AI models is crucial for banks to stay competitive and responsive to evolving customer needs and regulatory environments.


For AI in banking, it is crucial to have mechanisms for cleaning, accessing, and storing data effectively. The data store should be straightforward to access and have appropriate permission settings to maintain data security and privacy. Accessibility to high-quality, clean data is key for training accurate and reliable AI models.

Historically, making data available to data scientists has been complex. Banks need to balance the traditional analytics capabilities of a data warehouse with the flexibility of data lakes in order to cater to diverse analytical needs. This dual approach allows for both structured and unstructured data analysis, vital for comprehensive AI applications in banking.


In AI governance within banking, data management is essential, and establishing clear data sourcing and model lineage is critical. This includes maintaining transparency in how data is collected and used to train AI models. Additionally, documenting model facts to assure fairness, explainability, and compliance is important, especially given the regulatory requirements in the banking sector.

Monitoring for model bias and drift is another key capability, forming part of model risk management. Banks must continuously assess and adjust their AI models to prevent inaccuracies and biases. Regular audits and reporting to regulators are also necessary to maintain compliance and transparency in AI usage.


Incorporating Machine Learning Operations (MLOps) is crucial in the operational aspect of AI in banking. MLOps involves the management and continuous improvement of AI models, helping them remain effective and accurate over time. This includes deploying, monitoring, and maintaining AI models in a way that is scalable and efficient.

MLOps also supports the collaborative aspect of AI development, involving various teams from data scientists to IT professionals. This collaboration helps confirm that AI models are not only technically sound but also aligned with the bank's business goals and compliance standards.


The ability to integrate AI with applications to deliver AI-driven services is fundamental in banking. This involves combining AI models with existing banking applications to enhance customer experience and operational efficiency. For instance, integrating AI into customer service applications can provide more personalized and efficient service.

Exploiting a microservices architecture is beneficial for speed, time-to-market, and cost reduction. Microservices allow for the modular development of applications, making it easier to integrate AI and update services quickly in response to market changes or new regulatory requirements.


AI technology in banking must be adaptable, keeping pace with rapid advancements often driven by open-source communities. The ability to quickly incorporate new technologies, partners, and packages is key for maintaining a competitive edge.

This extensibility also implies that banking AI systems should be designed with future integrations in mind. As AI evolves, banks need to be able to adopt new methods and technologies to enhance their services and operations continuously. This requires a flexible platform and an organizational culture that embraces continuous learning and adaptation.

Navigating the integration of AI in banking requires not only understanding the necessary capabilities but also finding the right partners and tools to facilitate this journey. Red Hat emerges as a key player in this space, offering solutions tailored to the unique needs of AI in banking.

Red Hat brings together data scientists, developers, and operations on a cohesive platform so you can streamline the delivery of AI-driven banking services. Red Hat and IBM offer the transparency and control that is important for banks. Our commitment to scalability and security aligns with the needs of AI in banking, helping institutions navigate the complexities of AI integration while staying ahead in a rapidly evolving technological landscape.

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Red Hat OpenShift

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Red Hat partnerships

Red Hat's robust partner ecosystem connects you with solutions for creating, deploying, and managing deep learning models for AI-powered intelligent applications. Red Hat AI partners help to complete the AI pipeline with solutions ranging from data integration and preparation to AI model development and training, to model serving and inferencing based on new data.

These partners include:

  • IBM and watsonx. watsonx is a next-generation, enterprise-ready AI and data platform designed to multiply the impact of AI across your business.
  • Nvidia. Red Hat and Nvidia are committed to open source collaboration to accelerate the delivery of AI-powered applications.
  • SAS. Red Hat and SAS collaborate to enable organizations to use open, hybrid cloud technologies and analytical capabilities to improve business-level intelligence.
  • And many more.

Our focus on providing robust, scalable platforms that support the development and deployment of AI and ML models aligns with the dynamic requirements of modern banking. By fostering strong partnerships and offering adaptable solutions, Red Hat helps financial institutions navigate the complexities of AI adoption.

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InstructLab is an open source project for enhancing large language models (LLMs).

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Machine learning is the technique of training a computer to find patterns, make predictions, and learn from experience without being explicitly programmed.


What are foundation models?

A foundation model is a type of machine learning (ML) model that is pre-trained to perform a range of tasks. 

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